Uncertainty with UAV Search of Multiple Goal-oriented Targets
This addresses the challenge of efficient UAV-based search in uncertain environments for applications like surveillance or rescue, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of a UAV team searching for multiple moving targets with uncertain locations, destinations, sensing, and movement models, proposing a real-time algorithmic framework that combines entropy and stochastic-temporal belief to optimize detection probability, and shows significant performance improvements in empirical evaluations compared to other solutions.
This paper considers the complex problem of a team of UAVs searching targets under uncertainty. The goal of the UAV team is to find all of the moving targets as quickly as possible before they arrive at their selected goal. The uncertainty considered is threefold: First, the UAVs do not know the targets' locations and destinations. Second, the sensing capabilities of the UAVs are not perfect. Third, the targets' movement model is unknown. We suggest a real-time algorithmic framework for the UAVs, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful detection of all of the targets. We have empirically evaluated the algorithmic framework, and have shown its efficiency and significant performance improvement compared to other solutions. Furthermore, we have evaluated our framework using Peer Designed Agents (PDAs), which are computer agents that simulate targets and show that our algorithmic framework outperforms other solutions in this scenario.